--- permalink: /textanalysis/ keywords: fastai description: "Awesome summary" title: Text analysis toc: false branch: master badges: true comments: true categories: [text analysis, sentiment analysis, wordclouds] image: images/some_folder/your_image.png hide: false search_exclude: false metadata_key1: metadata_value1 metadata_key2: metadata_value2 nb_path: _notebooks\05_Text_Analysis.ipynb layout: notebook ---
This section regarding text analysis is divided into two parts: namely wordclouds and sentiment analysis. Both the extracted wiki pages and the character dialogues will be used and it will be investigated how wordclouds and sentiment analysis will differ based on the two different data sets.
First, we will take a look at word clouds. As mentioned before, both the extracted wiki pages and the full series dialogue will be investigated. We will start by generating wordclouds for characters of interest. Here, we have selected the characters: Jon Snow, Arya Stark, Bronn, Brienne of Tarth and Jaime Lannister. The first step in generating the wordclouds is to compute the term frequeny-inverse document frequency (TF-IDF) for our respective text corpus, i.e. the wiki pages and episode dialogues. For further explanation of the TF-IDF and it's computation we refer to the Explainer Notebook.
Now, let's take a look at the generated wordclouds for the selected characters.